Liu et al. [1]} also presented a review of literature on the applications of Artificial Intelligence algorithms for the fault detection of rotating machinery with a focus on traditional machine learning methods such as Naive Bayes classifier, K-Nearest Neighbours (k-NN), Support Vector Machines (SVM), and Artificial Neural Network algorithms. In earlier years of intelligent fault diagnosis, traditional machine learning approaches involved collecting raw sensor data of various fault types, extracting features from the collected data, and developing diagnosis models from the features to automatically recognize machines' health status. Although traditional machine learning methods can automate the fault detection process, these approaches cannot handle increasingly large data due to their low generalization performance, thereby reducing their accuracy in fault diagnosis. For instance, support vector machine classifiers can be applied to classification and regression problems. However, they do not perform well when applied to multi-class classifications or pair-wise classification problems. Some approaches like SVMs are computationally expensive and can not deal massive industrial data efficiently [2]}.
